The present invention relates to physiological monitoring and, more particularly, to noise handling in acoustic ambulatory respiration monitoring.
Ambulatory respiration monitoring can be helpful in maintaining the respiratory health of people as they go about their daily lives. For example, ambulatory respiration monitoring can enable prompt discovery of a problem with the respiratory health of a person who suffers from a chronic pulmonary disease or works in hazardous environment so that the person can obtain timely treatment. Since respiration sound contains vital signs such as respiration rate and heart rate, ambulatory respiration monitoring can also be applied to other fields such as senior monitoring and sleep monitoring.
Ambulatory respiration monitoring often invokes the respiration sound method, sometimes called auscultation. In the respiration sound method, an acoustic transducer mounted on the body of the person being monitored captures and acquires an acoustic signal recording lung sounds. The sound transducer is typically placed over the suprasternal notch or at the lateral neck near the pharynx because lung sounds captured in that region typically have a high signal-to-noise ratio and a high sensitivity to variation in flow. Once the acoustic signal with recorded lung sounds has been generated, respiration phases are identified in the acoustic signal and respiration parameter estimates (e.g., respiration rate, inspiration/expiration ratio) are calculated. Respiration health status information based on respiration parameter estimates may then be outputted locally to the monitored person or remotely to a clinician.
One problem commonly encountered in acoustic ambulatory respiration monitoring is parameter estimation error caused by noise. An acoustic signal that records lung sounds in a mobile environment can be disrupted by several types of noise, such as long-term, moderate amplitude noise introduced by the surrounding environment, or short-term, high amplitude noise introduced by impulse events such as coughing or sneezing. Regardless of the source, noise can result in erroneous estimation of respiration parameters and outputting of erroneous respiration health status information. In turn, reliance on outputted information that is erroneous can have serious adverse consequences on the health of the monitored person. For example, such information can lead the person or his or her clinician to improperly diagnose respiration health status and cause the person to undergo treatment that is not medically indicated or forego treatment that is medically indicated.
Known approaches to combating noise-induced parameter estimation error, such as using a reference microphone to measure environmental noise and attempting to cancel the noise through differentiation, have added complexity to ambulatory monitoring systems and at best offered only piecemeal solutions.